Recommendation method and device based on deep learning

A technology of deep learning and recommendation method, applied in the field of recommendation, it can solve the problems that the recommended objects are not screened, the recommendation with high accuracy and high satisfaction cannot be achieved, and the recommendation efficiency is low, so as to achieve good noise immunity and effectiveness. Effect

Pending Publication Date: 2021-08-06
杭州腾纵科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is no screening of recommended objects, and no consideration is given to whether users accept these recommendations. These are the reasons for the low recommendation effici

Method used

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  • Recommendation method and device based on deep learning
  • Recommendation method and device based on deep learning
  • Recommendation method and device based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0055] Example 1

[0056] like figure 1 As shown, a recommended method based on deep learning, including the following steps:

[0057] S110, acquire multiple user portraits and multiple commodity properties, and lock the target user according to the plurality of user portraits;

[0058] S120, extract the display of the target user and the display characteristics of the plurality of commodities, and then generate a list of recommendations;

[0059] S130, using a depth learning model to learn the hidden features of the target user and the hidden features of the multiple commodity properties, and predict the target user's score of the recommended list according to the syndrome;

[0060] S140, the product of the recommendation list is pre-recommended to determine whether the target user receives it. If so, the score of the recommendation list is recommended in priority.

[0061] In Embodiment 1, a plurality of user portraits are acquired, and the plurality of user portraits include us...

Example Embodiment

[0062] Example 2

[0063] like figure 2 As shown, a recommended method based on deep learning, including:

[0064] S210, acquire multiple user portraits and multiple commodity properties, the user portrait includes behavioral features and preference features, the commodity attribute including product base attributes and product evaluation;

[0065] S220, set the multi-dimensional screening according to the behavior characteristics and preference features, and lock the target user according to the multi-dimensional screening;

[0066] S230, extract the display of the target user and the display characteristics of the plurality of commodity properties, and then generate a list of recommendations;

[0067] S240, lecture with a deep learning model to learn the hidden species of the target user and the hidden features of the multiple commodity properties, and predict the target user's score of the recommended list according to the synscies;

[0068] S250, the product of the recommendat...

Example Embodiment

[0070] Example 3

[0071] like image 3 As shown, a recommended method based on deep learning, including:

[0072] S310, acquire multiple user portraits and multiple commodity properties, and lock the target user according to the plurality of user portraits;

[0073] S320, the behavior characteristics of the target user are collected and the browsing records and search records of the plurality of goods are constructed, and the preference model of the target user is constructed;

[0074] S330, simultaneous collecting display features of the plurality of commodities attributes, and finds approximate goods according to the characteristics of the characteristics;

[0075] S340, a commodity cache stored in the approximate product and the preference model into a recommended list;

[0076] S350, using a deep learning model to learn the hidden species of the target user and the hidden features of the plurality of commodity properties, and predict the target user's score for the recommended...

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Abstract

The invention discloses a recommendation method based on deep learning, and the method comprises the steps: obtaining a plurality of user portraits and a plurality of commodity attributes, and locking a target user according to the plurality of user portraits; extracting the explicit features of the target user and the explicit features of the multiple commodity attributes and then processing the explicit features to generate a recommendation list; learning the hidden features of the target user and the hidden features of the plurality of commodity attributes by using a deep learning model, and predicting the score of the target user on the recommendation list according to the hidden features; pre-recommending the commodities in the recommendation list to the target user, determining whether the target user receives recommendation, and if yes, recommending related information of the commodities according to the score of the recommendation list and the priority. According to the method and device, multi-dimensional personalized screening is realized, the user autonomously selects whether to receive pushing or not, effective and accurate pushing is carried out, the dual requirements of the user and a merchant are met, and feature extraction based on deep learning has better noise immunity and effectiveness.

Description

technical field [0001] The present invention relates to the technical field of recommendation, in particular to a deep learning-based recommendation method and device. Background technique [0002] Classification of these models based on the form of input (methods with and without content information) and output (rating and ranking) has been proposed in the prior art. However, with the continuous emergence of new research results, this classification framework is no longer applicable, and a new inclusive framework is needed to better understand this research field. In the existing technology, the similarity between target users and approximate users is calculated. Degree, determine the approximate user with high similarity as the recommendation direction, and then recommend items to the target user by approximating the user's preferences. This is a recommendation method based on useless content information, and the recommendation method based on content information can be im...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06N3/04G06N3/08
CPCG06Q30/0631G06N3/08G06N3/045
Inventor 旷小勇梅俊华
Owner 杭州腾纵科技有限公司
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